NEURON: Query Execution Plan Meets Natural Language Processing For Augmenting DB Education

Proceedings of the 2019 International Conference on Management of Data(2019)

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摘要
A core component of a database systems course at the undergraduate level is the design and implementation of the query optimizer in an rdbms. The query optimization process produces aquery execution plan (qep ), which represents an execution strategy for an sql query. Unfortunately, in practice, it is often difficult for a student to comprehend a query execution strategy by perusing its qep, hindering her learning process. In this demonstration, we present a novel system called neuron that facilitates natural language interaction with qep s to enhance its understanding. neuron accepts an sql query (which may include joins, aggregation, nesting, among other things) as input, executes it, and generates a simplified natural language description (both in text and voice form) of the execution strategy deployed by the underlying rdbms. Furthermore, it facilitates understanding of various features related to a qep through anatural language question answering (nlqa ) framework. We advocate that such tool, world's first of its kind, can greatly enhance students' learning of the query optimization topic.
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关键词
database education, database usability, natural language text generation, query execution plan, question answering framework, relational database
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